---
type: entity
entity_type: organization
name: UK House of Lords Science and Technology Committee
domain: health
founded: N/A
status: active
headquarters: London, UK
related:
- All three major clinical AI regulatory tracks converged on adoption acceleration rather than safety evaluation in Q1 2026
reweave_edges:
- All three major clinical AI regulatory tracks converged on adoption acceleration rather than safety evaluation in Q1 2026|related|2026-04-07
---

# UK House of Lords Science and Technology Committee

Parliamentary committee responsible for examining science and technology policy in the United Kingdom. Conducts inquiries into emerging technologies and their regulatory frameworks.

## Timeline

- **2026-03-10** — Launched inquiry into "Innovation in the NHS — Personalised Medicine and AI" with explicit framing as adoption failure investigation rather than safety evaluation. Written evidence deadline April 20, 2026. First evidence session heard from academics including Professor Sir Mark Caulfield (100,000 Genomes Project).

## Key Activities

### 2026 NHS AI Inquiry

Inquiry scope examines:
- Current state of personalised medicine and AI
- Research infrastructure for development
- UK effectiveness in translating life sciences strengths into validated tools
- How proven innovations might be deployed across NHS
- Systematic barriers preventing deployment (procurement, clinical pathways, regulators)
- Whether appraisal and commissioning models are fit for purpose
- NHS fragmentation's contribution to uneven deployment
- Government role in strengthening research-industry-health service links

Critical framing: The inquiry asks "why does innovation fail to be adopted" not "is the innovation safe to deploy." This adoption-focused framing parallels broader regulatory capture patterns where the primary policy question is deployment speed rather than safety evaluation.

## Significance

The 2026 NHS AI inquiry represents the UK's most prominent current policy mechanism touching clinical AI. Its framing as an adoption failure inquiry (not a safety inquiry) suggests it is unlikely to produce recommendations that close the commercial-research gap on clinical AI safety evaluation.